AI-Based DeepGEM Tool for Predicting Gene Mutations in NSCLC Patients: A Randomized Controlled Study

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

NOT_YET_RECRUITING

Clinical Phase

NA

Total Enrollment

950 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-07-31

Study Completion Date

2028-07-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

This prospective, multicenter, randomized controlled trial aims to evaluate the clinical utility of DeepGEM, an artificial intelligence (AI)-based mutation prediction tool based on histopathological whole-slide images, in patients with non-small cell lung cancer (NSCLC). The study will assess whether DeepGEM can facilitate molecular testing, increase targeted therapy utilization, and improve survival outcomes in a real-world clinical setting. Patients with stage II-IV treatment-naïve NSCLC and qualified pathology slides for DeepGEM analysis will be enrolled. Eligible participants with AI-predicted EGFR, ALK, or ROS1 mutations will be randomized in a 4:1 ratio to either the DeepGEM-informed group (clinicians can access AI results to guide further testing and treatment) or the standard care group (clinicians are blinded to AI results and follow routine care).

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection

NCT06659601

Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection

COMPLETED

Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer

NCT05925738

Non-small Cell Lung Cancer Spread Through Air Space Visceral Pleural Invasion +1 more

UNKNOWN

Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

NCT06285058

Deep Learning Model Pathological Complete Response Non-small Cell Lung Cancer +1 more

NOT_YET_RECRUITING

Development and Demonstration of Intelligent Assessment Based on Multi-modal Information Fusion for Tumor Risk and Diagnosis and Treatment

NCT06653478

Artificial Intelligence Deep Learning Lung Cancer +6 more

RECRUITING

A Study of Real-world Treatment Patterns and Outcomes in Chinese Advanced NSCLC Patients Who Previously Received at Least 2 Line Treatments

NCT06617390

Non-small Cell Lung Cancer (NSCLC)

COMPLETED

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Non Small Cell Lung Caner

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

SINGLE

Investigators

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

DeepGEM-Informed Group

Participants whose clinicians are provided with DeepGEM-predicted mutation status (EGFR/ALK/ROS1). Physicians may choose to proceed with molecular testing and initiate targeted therapy based on AI predictions.

Group Type EXPERIMENTAL

DeepGEM-guided Molecular Testing and Treatment

Intervention Type OTHER

Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.

Standard Care Group

Participants whose clinicians do not receive DeepGEM prediction results and manage the case per standard diagnostic and treatment protocols without AI support.

Group Type ACTIVE_COMPARATOR

Standard Diagnostic Pathway

Intervention Type OTHER

DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

DeepGEM-guided Molecular Testing and Treatment

Artificial intelligence-based mutation prediction using DeepGEM to guide clinical decision-making for molecular testing and therapy selection.

Intervention Type OTHER

Standard Diagnostic Pathway

DeepGEM is used for eligibility screening, but its results are withheld. Clinicians manage patients per standard diagnostic and treatment practices.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Age between 18 and 75 years, inclusive, at the time of enrollment.
* Histologically or cytologically confirmed non-small cell lung cancer (NSCLC) with clinical stage II-IV as per the 8th edition of the AJCC staging system.
* Availability of qualified histopathological whole-slide images that can be reviewed through the KindMED system(DeepGEM).
* Successful mutation prediction of EGFR, ALK, or ROS1 by the DeepGEM AI tool.
* No prior systemic anti-cancer therapy, including chemotherapy, targeted therapy, or immunotherapy.
* Willing and able to comply with study requirements, including follow-up and treatment; written informed consent must be provided.

Exclusion Criteria

* Prior systemic anti-tumor therapy (chemotherapy, radiotherapy, targeted therapy-including but not limited to monoclonal antibodies or tyrosine kinase inhibitors) before enrollment.
* Failure of DeepGEM analysis or unqualified histopathological image quality.
* History of any other malignancy within the past 5 years, except for adequately treated basal cell carcinoma of the skin or in situ carcinoma (e.g., cervical carcinoma in situ).
* Cognitive or psychological barriers to understanding or accepting AI-based prediction or molecular testing.
* Pregnant or breastfeeding women, or women of childbearing potential who are not using effective contraception.
* Any other clinical condition that, in the opinion of the investigators, may interfere with the study protocol or compromise participant safety, including poor compliance with study procedures.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No